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Summary

IBM's acquisition of Merge Healthcare, a leading Vendor Neutral Archive (VNA) and Picture Archiving and Communication Systems (PACS) vendor, is significant for a number of reasons. The deal is one of the important steps IBM has taken to commercialize its cognitive computing solution, Watson. In December 2014, IBM established Watson Health as a separate business unit, reflecting the vendor's strategy to position healthcare as a growth priority. To strengthen the new business unit, IBM acquired two companies in April 2015: Phytel, a population health management company; and Explorys, a health-focused patient analytics vendor. These acquisitions, along with the recent acquisition of Merge, enable IBM to expose Watson to large amounts of patient data in all forms, including medical images. Moreover, IBM will benefit from Merge's existing customer relationships and installed base of PACS and VNAs in the US. The acquisition also provides the opportunity for IBM to consolidate Merge's leadership position in the growing VNA market.

Watson can influence modern medicine, with or without a diagnosis skill set

IBM has invested significant amounts of time and money in developing Watson. For IBM's efforts to bear fruit, the vendor must further commercialize Watson and convince the healthcare provider of Watson's value and differentiation. The acquisition brings access to both medical images and a sizeable installed base.

That the acquisition is expected to add $1bn to the vendor's balance sheet is of less importance than the fact that Merge has a PACS installed base of 7,500 sites in the US, and handles a staggering volume of 30 billion medical images on a daily basis. This provides a strong opportunity for Watson to learn from an abundant collection of medical images. IBM claims that 90% of patient data is in the form of medical images, which means it is critical for Watson to learn to observe, interpret, and make decisions using medical images.

It is safe to say that Watson, in its current form, lacks the capability to interpret medical images. However, Watson can play a crucial role in two areas. Firstly, hospitals – especially those with best-of-breed diagnostic imaging systems – are facing challenges in handling cross-departmental images. The ability to classify, segregate, and migrate images to on-premise/cloud data centers remains critical for effective collaboration between different departments in a hospital. Although VNA vendors such as TeraMedica are developing homegrown analytics solutions to handle large volumes of medical images, the challenges are formidable and well suited to Watson. This is also an area where IBM can leverage Watson cloud, for providers to store and analyze images using Watson cloud analytics.

Secondly, Watson can assist radiologists in the interpretation of medical images to detect early signs of diseases such as cancer and coronary heart disease. Once skilled in gathering and identifying medical images, Watson could be used for identifying early signs of diseases and disease patterns, and for the grouping of images based on specific parameters. Furthermore, Watson could be used to compare medical images with historic data of population groups with similar medical conditions, and to cross-reference medical images with other forms of patient data. This will enable radiologists to handle greater volumes of medical images besides driving evidence-based medicine.

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